THEMIS.COG: Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups

THEMIS.COG：协作群体中身份和情感的理论和实证建模

• 批准号: 1723608
• 负责人: Kimberly Rogers
• 金额: $ 17.47万
• 依托单位: Dartmouth College
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-03-15 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1723608&HistoricalAwards=false
• 关键词: THEMIS.COG; Theoretical; Empirical; Modeling; Identity

The proposed research will provide new insights into the dynamics of self-organized collaborations, in which people come together organically to work on a common problem, without prompting by a third party. Understanding the social forces behind self-organized collaboration is increasingly important in today's society. Technological and social innovations are increasingly generated through informal, distributed collaboration processes, rather than in formal, hierarchical organizations. This project will use a data-driven approach to explore the social and psychological mechanisms that motivate self-organized collaborations and determine their likelihood of success or failure, focusing on the example of open, collaborative software development in online collaborative networks like GitHub (github.com). We offer a mathematically precise model for predicting and testing collaborative dynamics, which builds on a long tradition of sociological theory and research. By accounting for noise and uncertainty in processing social events, this work will make it possible to study the reach of social psychological mechanisms to forms of interaction other than face-to-face communication, where interpretations of events are far less certain, and to cross-cultural interactions.The research builds on a powerful sociological theory of group processes known as affect control theory (ACT) and a recent probabilistic generalization of it, Bayesian affect control theory (BayesACT). BayesACT applies insights from Bayesian probability theory to explain how people learn and adjust meanings through social experience, and show how stable interaction dynamics emerge from individuals' uncertain and noisy perceptions of their own and others' identities. The model rests on the idea that humans are motivated in their social interactions by affective alignment: They strive for their social experiences to be coherent at a deep, emotional level with their sense of identity and the cultural beliefs they share with others. BayesACT makes explicit predictions about online interactions in a collaborative group, based on the notion that each group member holds an identity that is learnable, mathematically describable, and complementary to those of other group members. These predictions allow for deeper and more focused data mining, enabling the identification of interactions of specific types in order to answer questions about the very nature of collaboration within online collaborative networks. The knowledge generated through this project will be widely disseminated online, and through professional presentations and publications. In addition, the project will provide training and mentoring for a postdoctoral fellow and a diverse group of undergraduate research assistants. It will also contribute to education in the classroom.This award was made as part of Round 4 of the Digging Into Data Challenge, an international funding opportunity designed to foster research collaboration across countries and to encourage innovative approaches to analyzing large data sets in the social sciences and humanities. The U.S based researchers will collaborate with scholars in Canada and Germany to achieve the goals of this project.

这项拟议中的研究将为自组织合作的动态提供新的见解，在这种合作中，人们有机地聚集在一起，在没有第三方的推动下解决共同的问题。理解自组织协作背后的社会力量在当今社会越来越重要。技术和社会创新越来越多地通过非正式的分布式协作过程产生，而不是在正式的等级组织中产生。该项目将使用数据驱动的方法来探索激励自组织协作的社会和心理机制，并确定其成功或失败的可能性，重点是在GitHub（github.com）等在线协作网络中开放，协作软件开发的例子。我们提供了一个数学上精确的模型来预测和测试协作动态，这是建立在社会学理论和研究的悠久传统之上的。通过解释处理社会事件中的噪音和不确定性，这项工作将使研究社会心理机制对面对面交流以外的互动形式的影响成为可能，在面对面交流中，对事件的解释远不确定，这项研究建立在一个强大的社会学理论的群体过程被称为影响控制理论（ACT）贝叶斯影响控制理论（Bayesian affect control theory，BayesACT）。BayesACT应用贝叶斯概率理论的见解来解释人们如何通过社会经验来学习和调整意义，并展示稳定的互动动态如何从个人对自己和他人身份的不确定和嘈杂的感知中产生。该模型基于这样一种观点，即人类在社会交往中受到情感一致性的激励：他们努力使自己的社会经验在深层情感层面与他们的身份认同感和与他人共享的文化信仰保持一致。BayesACT对协作组中的在线交互进行了明确的预测，其基础是每个组成员都拥有一个可学习的、数学上可描述的身份，并且与其他组成员的身份互补。这些预测允许进行更深入和更集中的数据挖掘，从而能够识别特定类型的交互，以回答有关在线协作网络中协作性质的问题。通过该项目产生的知识将在网上以及通过专业介绍和出版物广泛传播。此外，该项目还将为博士后研究员和各种本科生研究助理提供培训和指导。该奖项是第四轮数据挖掘挑战赛的一部分，该挑战赛是一个国际资助机会，旨在促进各国之间的研究合作，并鼓励采用创新方法分析社会科学和人文科学中的大型数据集。美国的研究人员将与加拿大和德国的学者合作，以实现该项目的目标。

项目成果

Theoretical and Empirical Modeling of Identity and Sentiments in Collaborative Groups

协作群体中身份和情感的理论和实证建模

• 发表时间: 2021
• 期刊: SocArXiv
• 作者: Jesse Hoey, Meiyappan Nagappan

Modeling Impression Formation Processes Among Chinese and Americans

• DOI: 10.1177/00027642211066025
• 发表时间: 2022-01
• 期刊: American Behavioral Scientist
• 影响因子: 3.2
• 作者: Jun Zhao

Emotion and Interaction Processes in a Collaborative Online Network

协作在线网络中的情感和交互过程

• 发表时间: 2018
• 期刊: International Conference on Computational Social Science
• 作者: Deepak, Rishi;Hoey, Jesse;Nagappan, Mei;Rogers, Kimberly B.;Schroeder, Tobias
• 通讯作者: Schroeder, Tobias

Affective Dynamics and Control in Group Processes

群体过程中的情感动态和控制

• DOI: 10.1145/3279981.3279990
• 发表时间: 2018
• 期刊: Group Interaction Frontiers in Technology; Association for Computing Machinery
• 作者: Hoey, Jesse;Schröder, Tobias;Morgan, Jonathan H.;Rogers, Kimberly B.;Nagappan, Meiyappan
• 通讯作者: Nagappan, Meiyappan

Artificial Intelligence and Social Simulation: Studying Group Dynamics on a Massive Scale

• DOI: 10.1177/1046496418802362
• 发表时间: 2018-12-01
• 期刊: SMALL GROUP RESEARCH
• 影响因子: 3.7
• 作者: Hoey, Jesse;Schroeder, Tobias;Nagappan, Meiyappan
• 通讯作者: Nagappan, Meiyappan